cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota semarang,
Jawa tengah
INDONESIA
Media Statistika
Published by Universitas Diponegoro
ISSN : -     EISSN : 24770647     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Arjuna Subject : -
Articles 271 Documents
 22   23   24   25   26 
MODEL COMPARISON OF VECTOR AUTOREGRESSIVE RESHAPED AND SARIMA IN SEASONAL DATA (A CASE STUDY OF TEA PRODUCTION IN PT PERKEBUNAN NUSANTARA VIII INDONESIA) Ratnaningsih, Dewi Juliah; Adam, Fia Fridayanti
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.215-226

Abstract

PT Perkebunan Nusantara VIII (PTPN VIII) is a State-Owned Enterprise (BUMN). It operates in the plantation sector.  The leading commodity is tea.  The demand for tea produced by PTPN VIII is increasing. Thus, planning tea production is necessary. One of the production planning efforts is through forecasting based on previous data.  Tea  production data is time-series data.  It contains seasonal elements and is dependent on other locations. We will analyze data with these criteria  using space-time models, one of which is vector autoregressive (VAR). VAR models the relationship  between observations on certain variables at one time. It also models the observation of the variable itself at previous times. Additionally, VAR models  the relationship  between observations and other variables at previous times. This paper explains how to forecast tea  production. It uses the reconstituted VAR and Seasonal Autoregressive Moving Average (SARIMA) models. The results showed that the reconstituted VAR model was better than the SARIMA model in predicting tea production. The tea production prediction was at the Sedep and Santosa plantations in Bandung Regency.
MODELING OF WORLD CRUDE OIL PRICE BASED ON PULSE FUNCTION INTERVENTION ANALYSIS APPROACH Aliffia, Netha; Sediono, Sediono; Suliyanto, Suliyanto; Mardianto, M. Fariz Fadillah; Amelia, Dita
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.136-147

Abstract

Crude oil has important role in global economy, including Indonesia with considerable dependence on crude oil energy consumption. The increase in crude oil prices can be triggered by several factors, one of which is geopolitical conflict that occurred due to Russia's invasion of Ukraine on February 24, 2022. As the result, world crude oil prices rose above US$100 per barrel for the first time since 2014. Therefore, this study uses pulse function intervention analysis approach to evaluate the impact of certain events in predicting data over the next few periods. The pulse function is used because the intervention occurs at the moment t only. The data used starts from June 8, 2020 to September 19, 2022 on weekly basis with the proportion of training and testing data is 90:10. The best intervention model obtained is ARIMA (3,2,0) with b=0, s=1, r=2, and intervention point at T=91. The prediction results for the next 12 periods obtained MAPE value of 2.8982% and MSE of 10.2687. This study is expected to help reduce risks due to uncertainty in world crude oil prices and in line with the goals of the Sustainable Development Goals (SDGs) to ensure access to reliable, sustainable, and affordable energy.
COMPARISON OF SPATIAL WEIGHTED MATRIX BETWEEN POWER AND QUEEN ON THE SPATIAL EMPIRICAL BEST LINEAR UNBIASED PREDICTION MODEL (Study on Per Capita Expenditure in East Java Province in 2019) Luthfatul Amaliana; Andi Prasetya
MEDIA STATISTIKA Vol 16, No 1 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.1.100-111

Abstract

This study aims to make a comparison related to the spatial weighted matrix of power and queen in the SEBLUP model to estimate per capita expenditure in East Java in 2019. The data used is secondary data then the data were analyzed by the Spatial Empirical Best Linear Unbiased Prediction (SEBLUP). The results of this study indicate that the best spatial weighted matrix for estimating per capita expenditure in East Java using the SEBLUP model is the spatial weighted matrix of Queen, because it produces the smallest MSE value. In this study, the factors that significantly affect East Java's per capita expenditure are population density (X1), number of health facilities (X2), number of public elementary schools (X3), and the percentage of residents who have BPJS as the Fund Assistance Recipients (X5). The novelty of this study are combining multiple determinant factors that have demonstrated their substantial/significant effect on the average per capita expenditure and focusing on the regions characters in intermediate size (16<n<64).
GEOGRAPHICALLY WEIGHTED PANEL LOGISTIC REGRESSION SEMIPARAMETRIC MODELING ON POVERTY PROBLEM Aliyah Husnun Azizah; Nurjannah Nurjannah; Adji Achmad Rinaldo Fernandes; Rosita Hamdan
MEDIA STATISTIKA Vol 16, No 1 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.1.47-58

Abstract

Regression analysis is a statistical method used to investigate and model the relationship between variables. Furthermore, a regression analysis was developed that involved spatial aspects, namely Geographically Weighted Regression (GWR). GWR modeling consists of various types, one of which is Geographically Weighted Logistic Regression Semiparametric (GWLRS), an extension of the Logistic GWR model that produces local and global parameter estimators. In this study, it is proposed to combine the GWLRS model using panel data or Geographically Weighted Panel Logistic Regression Semiparametric (GWPLRS). The case study used in this research is the problem of poverty in 38 regions/cities in East Java, Indonesia, in 2018 – 2022 as seen from the Poverty Gap Index. The weights used in this research are the adaptive gaussian kernel weighting functions. The results of the parameter significance test show that the Human Development Index as global variable has a significant effect on each region/city.
MAKING BAYESIAN DISEASE MAPPING EASY AND INTERACTIVE: AN R SHINY APPLICATION Aswi, Aswi; Tiro, Muhammad Arif; Sudarmin, Sudarmin; Sukarna, Sukarna; Awi, Awi; Nurwan, Nurwan; Cramb, Susanna
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.148-159

Abstract

Spatial analysis of count data is important in epidemiology and other domains to identify spatial patterns. While Bayesian spatial models are a popular approach, they do require detailed knowledge of the process for model fitting, checking, and visualising results. Although a number of R packages are available to simplify running the model, there are still complexities when checking the model. This paper aims to provide a user-friendly and interactive R Shiny web application for the analysis of spatial data using Bayesian spatial Conditional Autoregressive Leroux models. The web application is built with the integration of the R packages shiny and CARBayes. The required data are the number of cases, population, and optionally some covariates for each region. In this case, we used Covid-19 data in 2021 in South Sulawesi province, Indonesia. This application enables fitting a Bayesian spatial CAR Leroux model under several hyperpriors and selecting the most appropriate through comparing several goodness of fit measures. The application also enables checking convergence, plus obtaining and visualising in an interactive map the relative risk of disease for each region.
COMPARISON OF LOGISTIC MODEL TREE AND RANDOM FOREST ON CLASSIFICATION FOR POVERTY IN INDONESIA Sukarna, Sukarna; Notodiputro, Khairil Anwar; Sartono, Bagus
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.112-123

Abstract

Classification methods are commonly employed to ensure homogeneous data within each group, facilitating the prediction of specific categories. The most frequently used classification models are Logistic Model Tree (LMT) and Random Forest (RF). This study aims to assess the accuracy rate in predicting the poverty status of regencies or towns across Indonesia, utilizing eight independent variables. The entire dataset was obtained from the official Central Bureau of Statistics website. The study investigates the accuracy of various iterations and combinations of training data. The results indicate that RF outperforms LMT in terms of accuracy, achieving a 100% improvement in iterations k=10 and k=500 and a 75% improvement in iteration k=100. Consequently, the RF proves to be more effective than the LMT for analyzing Indonesian poverty data, especially when incorporating all eight independent variables.
MAX-STABLE PROCESS WITH GEOMETRIC GAUSSIAN MODEL ON RAINFALL DATA IN SEMARANG CITY Arief Rachman Hakim; Rukun Santoso; Hasbi Yasin; Masithoh Yessi Rochayani
MEDIA STATISTIKA Vol 16, No 1 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.1.59-66

Abstract

Spatial extreme value (SEV) is a statistical technique for modeling extreme events at multiple locations with spatial dependencies between locations. High intensity rainfall can cause disasters such as floods and landslides. Rainfall modelling is needed as an early detection step. SEV was developed from the univariate Extreme Value Theory (EVT) method to become multivariate. This work uses the SEV approach, namely the Max-stable process, which is an extension of the multivariate EVT into infinite dimensions. There are 4 Max-stable process models, namely Smith, Schlater, Brown Resnik, and Geometric Gaussian, which have the Generalized Extreme Value (GEV) distribution. This study models extreme rainfall, using rainfall data in the city of Semarang. This research was carried out by modeling data using the Geometric Gaussian model. This method is developed from the Smith and Schlater model, so this model can get better modeling results than the previous model. The maximum extreme rainfall prediction results for the next two periods are Semarang climatology station 129.30 mm3, Ahmad Yani 121.40 mm3, and Tanjung Mas 111.00 mm3. The result from this study can be used as an alternative for the government for early detection of the possibility of extreme rainfall.
ENSEMBLE-BASED LOGISTIC REGRESSION ON HIGH-DIMENSIONAL DATA: A SIMULATION STUDY Widhianingsih, Tintrim Dwi Ary; Kuswanto, Heri; Prastyo, Dedy Dwi
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.13-24

Abstract

Dramatic computation growth encourages big data era, which induces data size escalation in various fields. Apart from huge sample size, cases arise high-dimensional data having more feature size than its samples. High-computing power compels the usage of modern approaches to deal with this typical dataset, while in practice, common logistic regression method is yet applied due to its simplicity and explainability. Applying logistic regression on high-dimensional data arises multicollinearity, overfitting, and computational complexity issues. Logistic Regression Ensemble (Lorens) and Ensemble Logistic Regression (ELR) are the logistic-regression-based alternative methods proposed to solve these problems. Lorens adopts ensemble concept with mutually exclusive feature partitions to form several subsets of data, while ELR involves feature selection in the algorithm by drawing part of features based on probability ranking value. This paper uncovers the effectiveness of Lorens and ELR applied to high-dimensional data classification through simulation study under three different scenarios, i.e., with feature size variation, for imbalanced high-dimensional data, and under multicollinearity conditions. Our simulation study reveals that ELR outperforms Lorens and obtains more stable performance over different feature sizes and imbalanced data settings. On the other hand, Lorens achieves more reliable performance than ELR on a simulation study with a multicollinearity issue.
A-OPTIMAL DESIGN IN NON-LINEAR MODELS TO INCREASE SILICON DIOXIDE PURITY LEVELS Weisha, Ghea; Erfiani, Erfiani; Irzaman, Irzaman; Syafitri, Utami Dyah
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.36-44

Abstract

Silica is the most mineral found on earth and is widely used in industry. Silica used in industry is usually silicon dioxide with a purity ≥ 95% and its often sold at a higher cost. To obtain the silica at a lower cost, silica extraction from biomass such as rice husk can be conducted. The purity of silica extracted from biomass tends to be lower than that of mineral silica. Silica with low purity can be increased by adjusting the temperature and the rate of temperature rise. This research aims to obtain the best design to determine the purity of silicon dioxide. The design of this study was generated based on the A-optimality criterion using the DETMAX algorithm. The A-optimality criterion is minimizing the trace of the variance-covariance of the parameter estimation. The best design points obtained using A-optimal design consist of three temperature groups: the minimum temperature of 800°C, the middle temperature of 850°C, and the maximum temperature of 900°C, with varying rates of temperature rise. Points were repeated at the temperature of 850°C, with rates of temperature rise of 1.67°C/min and 3.34°C/min. 
CONWAY-MAXWELL POISSON REGRESSION MODELING OF INFANT MORTALITY IN SOUTH SULAWESI Oktaviana, Oktaviana; Sanusi, Wahidah; Aswi, Aswi; Sukarna, Sukarna; Folorunso, Serifat Adedamola
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.45-56

Abstract

Overdispersion is a common problem in count data that can lead to inaccurate parameter estimates in Poisson regression models. Quasi-Poisson and negative binomial regressions are often used to address overdispersion but have limitations, especially with small samples. The Conway-Maxwell Poisson (CMP) regression model, an extension of the Poisson distribution, effectively addresses both overdispersion and underdispersion, even with limited data, due to additional parameters that better control data dispersion. The Infant Mortality Rate (IMR) is a critical public health indicator, reflecting healthcare quality and broader social, economic, and environmental factors. Accurate IMR estimation is essential for evaluating health policies. This study aims to (1) identify overdispersion in IMR data from South Sulawesi, (2) model IMR using CMP regression, and (3) identify factors influencing IMR. The dataset includes IMR, Low Birth Weight (LBW), diarrhea, asphyxia, pneumonia, and exclusive breastfeeding. Analysis showed significant overdispersion with a ratio of 4.639, making CMP the optimal model with an AIC of 186.845. Significant factors identified were LBW, asphyxia, pneumonia, and exclusive breastfeeding. These findings advance statistical methodologies for count data analysis and offer a more accurate approach to evaluating public health policies, supporting efforts to reduce infant mortality in South Sulawesi Province.

Page 24 of 28 | Total Record : 271

 22   23   24   25   26 

Filter by Year

2008 2025


Reset
Filter By Issues
All Issue Vol 18, No 1 (2025): Media Statistika Vol 17, No 2 (2024): Media Statistika Vol 17, No 1 (2024): Media Statistika Vol 16, No 2 (2023): Media Statistika Vol 16, No 1 (2023): Media Statistika Vol 15, No 2 (2022): Media Statistika Vol 15, No 1 (2022): Media Statistika Vol 14, No 2 (2021): Media Statistika Vol 14, No 1 (2021): Media Statistika Vol 13, No 2 (2020): Media Statistika Vol 13, No 1 (2020): Media Statistika Vol 12, No 2 (2019): Media Statistika Vol 12, No 1 (2019): Media Statistika Vol 11, No 2 (2018): Media Statistika Vol 11, No 1 (2018): Media Statistika Vol 10, No 2 (2017): Media Statistika Vol 10, No 1 (2017): Media Statistika Vol 9, No 2 (2016): Media Statistika Vol 9, No 1 (2016): Media Statistika Vol 8, No 2 (2015): Media Statistika Vol 8, No 1 (2015): Media Statistika Vol 7, No 2 (2014): Media Statistika Vol 7, No 1 (2014): Media Statistika Vol 6, No 2 (2013): Media Statistika Vol 6, No 1 (2013): Media Statistika Vol 5, No 2 (2012): Media Statistika Vol 5, No 1 (2012): Media Statistika Vol 4, No 2 (2011): Media Statistika Vol 4, No 1 (2011): Media Statistika Vol 3, No 2 (2010): Media Statistika Vol 3, No 1 (2010): Media Statistika Vol 2, No 2 (2009): Media Statistika Vol 2, No 1 (2009): Media Statistika Vol 1, No 2 (2008): Media Statistika Vol 1, No 1 (2008): Media Statistika More Issue